Merge branch 'master' into BayesianSetupUpdateParallel

.gitattributes

@@ -0,0 +1,23 @@
+# Set the default behavior, in case people don't have core.autocrlf set.
+* text=auto
+
+# Explicitly declare text files you want to always be normalized and converted
+# to native line endings on checkout.
+*.Rd    text
+*.Rmd	  text
+*.R  	  text
+*.r  	  text
+*.Rproj text
+*.[Rr]md   linguist-detectable
+*.yml    text
+
+
+# Denote all files that are truly binary and should not be modified.
+*.Rdata binary
+*.RData binary
+*.rda   binary
+*.rdb   binary
+*.rds   binary
+*.png binary
+*.jpg binary
+*.Rdx   binary

BayesianTools/CRAN.md

@@ -4,15 +4,15 @@ This file contains CRAN submission info / communication
 
 ### Submission 1
 
-This is minor update should re-instate BT to CRAN, after it was removed due to HTML validation problems connected to the recent switch to HTML5 for documentation pages in R 4.2.0, see also https://github.com/florianhartig/BayesianTools/issues/240. 
+This is a minor update that should bring BT back to CRAN after it was removed due to HTML validation problems associated with the recent switch to HTML5 for documentation pages in R 4.2.0, see also https://github.com/florianhartig/BayesianTools/issues/240. 
 
-I had tried to upload a fix to CRAN, which, however, was not accepted due to detritus problems under Windows dev, which, after a long search, turned up to be caused by some stray parallel notes https://github.com/florianhartig/BayesianTools/issues/244
+I had tried to upload a fix to CRAN, but it was not accepted due to detritus problems under Windows dev, which after a long search turned out to be caused by some stray parallel notes https://github.com/florianhartig/BayesianTools/issues/244.
 
-In any case, I hope this will re-instate the BT package to CRAN, sorry for the delay in fixing this. 
+In any case, I hope this will get the BT package back into CRAN, sorry for the delay in fixing this. 
 
 See NEWS for other changes.
 
-This release was tested without apparent problems under
+This release has been tested without apparent problems on
 
 * local MAC OS 13.1, R 4.2.1 Apple
 * http://win-builder.r-project.org/ - oldrelease / devel / release

BayesianTools/DESCRIPTION

@@ -3,7 +3,7 @@ Title: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics
 Version: 0.1.8
 Date: 2023-01-30
 Authors@R: c(person("Florian", "Hartig", email = "[email protected]", role = c("aut", "cre"), comment=c(ORCID="0000-0002-6255-9059")), person("Francesco", "Minunno", role = c("aut")), person("Stefan", " Paul", role = c("aut") ),  person("David", "Cameron", role = "ctb"), person("Tankred", "Ott", role = "ctb"), person("Maximilian", "Pichler", role = "ctb"))
-Description: General-purpose MCMC and SMC samplers, as well as plot and
+Description: General-purpose MCMC and SMC samplers, as well as plots and
     diagnostic functions for Bayesian statistics, with a particular focus on
     calibrating complex system models. Implemented samplers include various
     Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the

BayesianTools/R/BayesianTools.R

@@ -1,5 +1,6 @@
 #' @title BayesianTools
 #' @name BayesianTools
+#' @aliases BayesianTools-package
 #' @docType package
 #' @useDynLib BayesianTools, .registration = TRUE
 #' @description A package with general-purpose MCMC and SMC samplers,  as well as plots and diagnostic functions for Bayesian statistics

BayesianTools/R/classMcmcSampler.R

@@ -116,7 +116,7 @@ getSample.mcmcSampler <- function(sampler, parametersOnly = T, coda = F, start =
 #' @method summary mcmcSampler
 #' @author Stefan Paul
 #' @export
-summary.mcmcSampler <- function(object, ...){
+summary.mcmcSampler <- function(object, printCorrelation = "auto", ...){
   #codaChain = getSample(sampler, parametersOnly = parametersOnly, coda = T, ...)
   #summary(codaChain)
   #rejectionRate(sampler$codaChain)
@@ -133,8 +133,7 @@ summary.mcmcSampler <- function(object, ...){
 
   mcmcsampler <- sampler$settings$sampler
   runtime <- sampler$settings$runtime[3]
-  correlations <- round(cor(getSample(sampler)),3)
-
+
   chain <- getSample(sampler, parametersOnly = T, coda = T, ...)
   # chain <- getSample(sampler, parametersOnly = T, coda = T)
   if("mcmc.list" %in% class(chain)){
@@ -203,8 +202,11 @@ summary.mcmcSampler <- function(object, ...){
 
   try(cat("## DIC: ", round(DInf$DIC,3), "\n"), silent = TRUE)
   cat("## Convergence" ,"\n", "Gelman Rubin multivariate psrf: ", conv, "\n","\n")
-  cat("## Correlations", "\n")
-  print(correlations)
+  if(printCorrelation == TRUE){
+    correlations <- round(cor(getSample(sampler)),3)
+    cat("## Correlations", "\n")
+    print(correlations)    
+  }
 }
 
 #' @author Florian Hartig

BayesianTools/R/mcmcRun.R

@@ -263,7 +263,30 @@ runMCMC <- function(bayesianSetup , sampler = "DEzs", settings = NULL){
 #' @param settings optional list with parameters that will be used instead of the defaults
 #' @param sampler one of the samplers in \code{\link{runMCMC}} 
 #' @param check logical determines whether parameters should be checked for consistency
-#' @details see \code{\link{runMCMC}} 
+#' @details 
+#' 
+#' The following settings can be used for all MCMCs:
+#' 
+#' startValue (no default) start values for the MCMC. Note that DE family samplers require a matrix of #' start values. If startvalues are not provided, they are sampled from the prior.
+#' 
+#' iterations (10000) the MCMC iterations
+#' 
+#' burnin (0) burnin
+#' 
+#' thin (1) thinning while sampling
+#' 
+#' consoleUpdates (100) update frequency for console updates
+#' 
+#' parallel (NULL) whether parallelization is to be used
+#' 
+#' message (TRUE) if progress messages are to be printed
+#' 
+#' nrChains (1) the number of independent MCMC chains to be run. Note that this is not controlling the #' internal number of chains in population MCMCs such as DE, so if you run nrChains = 3 with a DEzs #' #' startValue that is a 4xparameter matrix (= 4 internal chains), you will run independent DEzs runs #' #' with 4 internal chains each.
+#' 
+#' For more details, see \code{\link{runMCMC}} 
+#' 
+#' 
+#' @example inst/examples/mcmcRun.R
 #' @export
 applySettingsDefault<-function(settings=NULL, sampler = "DEzs", check = FALSE){
 

BayesianTools/R/plotMarginals.R

@@ -170,7 +170,7 @@ marginalPlotDensity <- function(posteriorMat, priorMat = NULL, xrange = NULL, co
     mtext('Marginal parameter uncertainty', outer = TRUE, cex = 1.5)
 
   } else {
-    mfrow <- if (nPar < 16) getPanels(nPar) else c(4,4)
+    mfrow <- getPanels(nPar)
 
     op <- par(mfrow = mfrow, mar = c(4.5, 4, 5, 3), oma=c(3, 1.5, 2, 0), xpd=TRUE)
     on.exit(par(op))
@@ -282,7 +282,7 @@ marginalPlotViolin <- function(posteriorMat, priorMat = NULL, xrange = NULL, col
     mtext('Marginal parameter uncertainty', outer = TRUE, cex = 1.5)
 
   } else {
-    mfrow <- if (nPar < 16) getPanels(nPar) else c(4,4)
+    mfrow <- getPanels(nPar)
 
     op <- par(mfrow = mfrow, mar = c(4.5, 4.5, 5, 3), oma=c(3, 0, 2, 0), xpd=TRUE)
 

BayesianTools/inst/examples/mcmcRun.R

@@ -5,8 +5,9 @@ ll <- generateTestDensityMultiNormal(sigma = "no correlation")
 ## is the recommended way of using the runMCMC() function.
 bayesianSetup <- createBayesianSetup(likelihood = ll, lower = rep(-10, 3), upper = rep(10, 3))
 
-## Finally we can run the sampler and have a look
-settings = list(iterations = 1000, adapt = FALSE)
+## Finally we can run the sampler. To get possible settings
+## for a sampler, see help or run applySettingsDefault(sampler = "Metropolis")
+settings = list(iterations = 1000, adapt = FALSE) #
 out <- runMCMC(bayesianSetup = bayesianSetup, sampler = "Metropolis", settings = settings)
 
 ## out is of class bayesianOutput. There are various standard functions 
@@ -21,3 +22,5 @@ summary(out)
 # for plotting and analysis
 
 codaObject = getSample(out, start = 500, coda = TRUE)
+
+